OpenRaiser/Intern-Atlas huggingface dataset:
Matching Dyadic Distributions to Channels
Venue: arXiv cs.IT 2010
Year: 2010

Many communication channels with discrete input have non-uniform capacity achieving probability mass functions (PMF). By parsing a stream of independent and equiprobable bits according to a full prefix-free code, a modu-lator can generate dyadic PMFs at the channel input. In this work, we show that for discrete memoryless channels and for memoryless discrete noiseless channels, searching for good dyadic input PMFs is equivalent to minimizing the Kullback-Leibler distance between a dyadic PMF and a weighted version of the capacity achieving PMF. We define a new algorithm called Geometric Huffman Coding (GHC) and prove that GHC finds the optimal dyadic PMF in O(m \log m) steps where m is the number of input symbols of the considered channel. Furthermore, we prove that by generating dyadic PMFs of blocks of consecutive input symbols, GHC achieves capacity when the block length goes to infinity.
An extensible web interface for databases and its application to storing biochemical data
Venue: arXiv cs.PL 2010
Year: 2010

This paper presents a generic web-based database interface implemented in Prolog. We discuss the advantages of the implementation platform and demonstrate the system's applicability in providing access to integrated biochemical data. Our system exploits two libraries of SWI-Prolog to create a schema-transparent interface within a relational setting. As is expected in declarative programming, the interface was written with minimal programming effort due to the high level of the language and its suitability to the task. We highlight two of Prolog's features that are well suited to the task at hand: term representation of structured documents and relational nature of Prolog which facilitates transparent integration of relational databases. Although we developed the system for accessing in-house biochemical and genomic data the interface is generic and provides a number of extensible features. We describe some of these features with references to our research databases. Finally we outline an in-house library that facilitates interaction between Prolog and the R statistical package. We describe how it has been employed in the present context to store output from statistical analysis on to the database.
Towards a Study of Meta-Predicate Semantics
Venue: arXiv cs.PL 2010
Year: 2010

We describe and compare design choices for meta-predicate semantics, as found in representative Prolog module systems and in Logtalk. We look at the consequences of these design choices from a pragmatic perspective, discussing explicit qualification semantics, computational reflection support, expressiveness of meta-predicate declarations, safety of meta-predicate definitions, portability of meta-predicate definitions, and meta-predicate performance. Our aim is to provide useful insight for debating meta-predicate semantics and portability issues based on actual implementations and common usage patterns.
DNF Sampling for ProbLog Inference
Venue: arXiv cs.LO 2010
Year: 2010

Inference in probabilistic logic languages such as ProbLog, an extension of Prolog with probabilistic facts, is often based on a reduction to a propositional formula in DNF. Calculating the probability of such a formula involves the disjoint-sum-problem, which is computationally hard. In this work we introduce a new approximation method for ProbLog inference which exploits the DNF to focus sampling. While this DNF sampling technique has been applied to a variety of tasks before, to the best of our knowledge it has not been used for inference in probabilistic logic systems. The paper also presents an experimental comparison with another sampling based inference method previously introduced for ProbLog.
Secure Lossy Source Coding with Side Information at the Decoders
Venue: arXiv cs.IT 2010
Year: 2010

This paper investigates the problem of secure lossy source coding in the presence of an eavesdropper with arbitrary correlated side informations at the legitimate decoder (referred to as Bob) and the eavesdropper (referred to as Eve). This scenario consists of an encoder that wishes to compress a source to satisfy the desired requirements on: (i) the distortion level at Bob and (ii) the equivocation rate at Eve. It is assumed that the decoders have access to correlated sources as side information. For instance, this problem can be seen as a generalization of the well-known Wyner-Ziv problem taking into account the security requirements. A complete characterization of the rate-distortion-equivocation region for the case of arbitrary correlated side informations at the decoders is derived. Several special cases of interest and an application example to secure lossy source coding of binary sources in the presence of binary and ternary side informations are also considered. It is shown that the statistical differences between the side information at the decoders and the presence of non-zero distortion at the legitimate decoder can be useful in terms of secrecy. Applications of these results arise in a variety of distributed sensor network scenarios.
Malicious cryptography techniques for unreversable (malicious or not) binaries
Venue: arXiv cs.CR 2010
Year: 2010

Fighting against computer malware require a mandatory step of reverse engineering. As soon as the code has been disassemblied/decompiled (including a dynamic analysis step), there is a hope to understand what the malware actually does and to implement a detection mean. This also applies to protection of software whenever one wishes to analyze them. In this paper, we show how to amour code in such a way that reserse engineering techniques (static and dymanic) are absolutely impossible by combining malicious cryptography techniques developped in our laboratory and new types of programming (k-ary codes). Suitable encryption algorithms combined with new cryptanalytic approaches to ease the protection of (malicious or not) binaries, enable to provide both total code armouring and large scale polymorphic features at the same time. A simple 400 Kb of executable code enables to produce a binary code and around $2^{140}$ mutated forms natively while going far beyond the old concept of decryptor.
Robust Transceiver Design for K-Pairs Quasi-Static MIMO Interference Channels via Semi-Definite Relaxation
Venue: arXiv cs.IT 2010
Year: 2010

In this paper, we propose a robust transceiver design for the K-pair quasi-static MIMO interference channel. Each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the k-th transmitter sends L_k independent data streams to the desired receiver. In the literature, there exist a variety of theoretically promising transceiver designs for the interference channel such as interference alignment-based schemes, which have feasibility and practical limitations. In order to address practical system issues and requirements, we consider a transceiver design that enforces robustness against imperfect channel state information (CSI) as well as fair performance among the users in the interference channel. Specifically, we formulate the transceiver design as an optimization problem to maximize the worst-case signal-to-interference-plus-noise ratio among all users. We devise a low complexity iterative algorithm based on alternative optimization and semi-definite relaxation techniques. Numerical results verify the advantages of incorporating into transceiver design for the interference channel important practical issues such as CSI uncertainty and fairness performance.
Game semantics for first-order logic
Venue: arXiv cs.LO 2010
Year: 2010

We refine HO/N game semantics with an additional notion of pointer (mu-pointers) and extend it to first-order classical logic with completeness results. We use a Church style extension of Parigot's lambda-mu-calculus to represent proofs of first-order classical logic. We present some relations with Krivine's classical realizability and applications to type isomorphisms.
Architecture and Algorithms for an Airborne Network
Venue: arXiv cs.NI 2010
Year: 2010

The U.S. Air Force currently is in the process of developing an Airborne Network (AN) to provide support to its combat aircrafts on a mission. The reliability needed for continuous operation of an AN is difficult to achieve through completely infrastructure-less mobile ad hoc networks. In this paper we first propose an architecture for an AN where airborne networking platforms (ANPs - aircrafts, UAVs and satellites) form the backbone of the AN. In this architecture, the ANPs can be viewed as mobile base stations and the combat aircrafts on a mission as mobile clients. The combat aircrafts on a mission move through a space called air corridor. The goal of the AN design is to form a backbone network with the ANPs with two properties: (i) the backbone network remains connected at all times, even though the topology of the network changes with the movement of the ANPs, and (ii) the entire 3D space of the air corridor is under radio coverage at all times by the continuously moving ANPs. In addition to proposing an architecture for an AN, the contributions of the paper include, development of an algorithm that finds the velocity and transmission range of the ANPs so that the dynamically changing backbone network remains connected at all times, development of a routing algorithm that ensures a connection between the source-destination node pair with the fewest number of path switching, given the dimensions of the air corridor and the radius of the coverage sphere associated with an ANP, development of an algorithm that finds the fewest number of ANPs required to provide complete coverage of the air corridor at all times, development of an algorithm that provides connected-coverage to the air corridor at all times, and development of a visualization tool that depicts the movement patterns of the ANPs and the resulting dynamic graph and the coverage volume of the backbone network.
A Constructive Algorithm for Feedforward Neural Networks for Medical Diagnostic Reasoning
Venue: arXiv cs.NE 2010
Year: 2010

This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. Our research describes a constructive neural network algorithm with backpropagation; offer an approach for the incremental construction of nearminimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. Our algorithm was tested on several benchmarking classification problems including Cancer1, Heart, and Diabetes with good generalization ability.
Medical diagnosis using neural network
Venue: arXiv cs.NE 2010
Year: 2010

This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. This paper describes a modified feedforward neural network constructive algorithm (MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm with backpropagation; offer an approach for the incremental construction of near-minimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. The MFNNCA was tested on several benchmarking classification problems including the cancer, heart disease and diabetes. Experimental results show that the MFNNCA can produce optimal neural network architecture with good generalization ability.
Using the PALS Architecture to Verify a Distributed Topology Control Protocol for Wireless Multi-Hop Networks in the Presence of Node Failures
Venue: arXiv cs.LO 2010
Year: 2010

The PALS architecture reduces distributed, real-time asynchronous system design to the design of a synchronous system under reasonable requirements. Assuming logical synchrony leads to fewer system behaviors and provides a conceptually simpler paradigm for engineering purposes. One of the current limitations of the framework is that from a set of independent "synchronous machines", one must compose the entire synchronous system by hand, which is tedious and error-prone. We use Maude's meta-level to automatically generate a synchronous composition from user-provided component machines and a description of how the machines communicate with each other. We then use the new capabilities to verify the correctness of a distributed topology control protocol for wireless networks in the presence of nodes that may fail.
TCP-controlled Long File Transfer Throughput in Multirate WLANs with Nonzero Round Trip Propagation Delays
Venue: arXiv cs.NI 2010
Year: 2010

In a multirate WLAN with a single access point (AP) and several stations (STAs), we obtain analytical expressions for TCP-controlled long file transfer throughputs allowing nonzero propagation delays between the file server and STAs. We extend our earlier work in [3] to obtain AP and STA throughputs in a multirate WLAN, and use these in a closed BCMP queueing network model to obtain TCP throughputs. Simulation show that our approach is able to predict observed throughputs with a high degree of accuracy.
RGANN: An Efficient Algorithm to Extract Rules from ANNs
Venue: arXiv cs.NE 2010
Year: 2010

This paper describes an efficient rule generation algorithm, called rule generation from artificial neural networks (RGANN) to generate symbolic rules from ANNs. Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features. They are explicit, understandable and verifiable by domain experts, and can be modified, extended and passed on as modular knowledge. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Comparing them to the symbolic rules generated by other methods supports explicitness of the generated rules. Generated rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, including breast cancer, wine, season, golf-playing, and lenses classification demonstrate the effectiveness of the proposed approach with good generalization ability.
Speaker Identification using MFCC-Domain Support Vector Machine
Venue: arXiv cs.LG 2010
Year: 2010

Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). In this work, melfrequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network. This work firstly used sequential minimum optimization (SMO) learning technique for SVM that improve performance over traditional techniques Chunking, Osuna. The cepstrum coefficients representing the speaker characteristics of a speech segment are computed by nonlinear filter bank analysis and discrete cosine transform. The speaker identification ability and convergence speed of the SVMs are investigated for different combinations of features. Extensive experimental results on several samples show the effectiveness of the proposed approach.
Universal Numeric Segmented Display
Venue: arXiv cs.AR 2010
Year: 2010

Segmentation display plays a vital role to display numerals. But in today's world matrix display is also used in displaying numerals. Because numerals has lots of curve edges which is better supported by matrix display. But as matrix display is costly and complex to implement and also needs more memory, segment display is generally used to display numerals. But as there is yet no proposed compact display architecture to display multiple language numerals at a time, this paper proposes uniform display architecture to display multiple language digits and general mathematical expressions with higher accuracy and simplicity by using a 18-segment display, which is an improvement over the 16 segment display.
REx: An Efficient Rule Generator
Venue: arXiv cs.NE 2010
Year: 2010

This paper describes an efficient algorithm REx for generating symbolic rules from artificial neural network (ANN). Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features. They are explicit, understandable and verifiable by domain experts, and can be modified, extended and passed on as modular knowledge. REx exploits the first order information in the data and finds shortest sufficient conditions for a rule of a class that can differentiate it from patterns of other classes. It can generate concise and perfect rules in the sense that the error rate of the rules is not worse than the inconsistency rate found in the original data. An important feature of rule extraction algorithm, REx, is its recursive nature. They are concise, comprehensible, order insensitive and do not involve any weight values. Extensive experimental studies on several benchmark classification problems, such as breast cancer, iris, season, and golf-playing, demonstrate the effectiveness of the proposed approach with good generalization ability.
Demonstrating a Service-Enhanced Retrieval System
Venue: arXiv cs.IR 2010
Year: 2010

This paper is a short description of an information retrieval system enhanced by three model driven retrieval services: (1) co-word analysis based query expansion, re-ranking via (2) Bradfordizing and (3) author centrality. The different services each favor quite other - but still relevant - documents than pure term-frequency based rankings. Each service can be interactively combined with each other to allow an iterative retrieval refinement.
Bus Protocols: MSC-Based Specifications and Translation into Program of Verification Tool for Formal Verification
Venue: arXiv cs.SE 2010
Year: 2010

Message Sequence Charts (MSCs) are an appealing visual formalism mainly used in the early stages of system design to capture the system requirements. However, if we move towards an implementation, an executable specifications related in some fashion to the MSC-based requirements must be obtained. The MSCs can be used effectively to specify the bus protocol in the way where high-level transition systems is used to capture the control flow of the system components of the protocol and MSCs to describe the non-atomic component interactions. This system of specification is amenable to formal verification. In this paper, we present the way how we can specify the bus protocols using MSCs and how these specifications can be translated into program of verification tool (we have used Symbolic Model Verifier (SMV)) for the use of formal verification. We have contributed to the following tasks in this respect. Firstly, the way to specify the protocol using MSC has been presented. Secondly, a translator that translates the specifications (described in a textual input file) into SMV programs has been constructed. Finally, we have presented the verification result of the AMBA bus protocol using the SMV program found through the translation process. The SMV program found through the translation process can be used in order to automatically verify various properties of any bus protocol specified.
General Scaled Support Vector Machines
Venue: arXiv cs.AI 2010
Year: 2010

Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin. Therefore, C-SVM loses robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the distribution of the entire data. But existing work can only be applied for 1-D case. In this paper, we propose a simple and efficient method called General Scaled SVM (GS-SVM) to extend the existing approach to multi-dimensional case. Our method translates the hyperplane according to the distribution of data projected on the normal vector of the hyperplane. Compared with C-SVM, GS-SVM has better performance on several data sets.
A Novel Approach for Cardiac Disease Prediction and Classification Using Intelligent Agents
Venue: arXiv cs.MA 2010
Year: 2010

The goal is to develop a novel approach for cardiac disease prediction and diagnosis using intelligent agents. Initially the symptoms are preprocessed using filter and wrapper based agents. The filter removes the missing or irrelevant symptoms. Wrapper is used to extract the data in the data set according to the threshold limits. Dependency of each symptom is identified using dependency checker agent. The classification is based on the prior and posterior probability of the symptoms with the evidence value. Finally the symptoms are classified in to five classes namely absence, starting, mild, moderate and serious. Using the cooperative approach the cardiac problem is solved and verified.
Approximability of Capacitated Network Design
Venue: arXiv cs.DS 2010
Year: 2010

In the {\em capacitated} survivable network design problem (Cap-SNDP), we are given an undirected multi-graph where each edge has a capacity and a cost. The goal is to find a minimum cost subset of edges that satisfies a given set of pairwise minimum-cut requirements. Unlike its classical special case of SNDP when all capacities are unit, the approximability of Cap-SNDP is not well understood; even in very restricted settings no known algorithm achieves a $o(m)$ approximation, where $m$ is the number of edges in the graph. In this paper, we obtain several new results and insights into the approximability of Cap-SNDP.
Morphological dilation image coding with context weights prediction
Venue: arXiv cs.IT 2010
Year: 2010

This paper proposes an adaptive morphological dilation image coding with context weights prediction. The new dilation method is not to use fixed models, but to decide whether a coefficient needs to be dilated or not according to the coefficient's predicted significance degree. It includes two key dilation technologies: 1) controlling dilation process with context weights to reduce the output of insignificant coefficients, and 2) using variable-length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability. Moreover, we also propose a novel context weight strategy to predict coefficient's significance degree more accurately, which serves for two dilation technologies. Experimental results show that our proposed method outperforms the state of the art image coding algorithms available today.